Create Corpus API Definition
The Create Corpus API lets you create a corpus to store and manage your documents. A corpus is a container for documents and their associated metadata. When creating a corpus, you can specify various settings such as the corpus key, name, description, encoder, and filter attributes.
Corpus Object
When you create a corpus object, the key property is required to uniquely
identify the corpus. The name parameter is optional and defaults to the
value of key. The optional description properties lets you provide
additional information about the corpus.
You can specify whether to treat queries or documents in the corpus as
questions or answers using the queries_are_answers and documents_are_questions
boolean properties. These settings affect the semantics of the encoder used at
query time and indexing time.
The encoder_name property allows you to choose the encoder for the corpus.
If not specified, it defaults to the latest Vectara encoder.
The encoder_id property has been deprecated. Use the encoder_name
property instead.
In order to reference metadata in filter expressions, the attributes
are declared at creation time in the filter_attributes array. You can add,
edit, and remove filter attributes from the Console UI in the Corpora Settings,
or with the Replace Filters Attributes API definition.
Pro and Enterprise users can specify custom_dimensions to allow weighting of
document parts during indexing and querying. Like filter attributes, custom
dimensions cannot be changed after corpus creation. For more information, see
Custom Dimensions. Custom dimensions cannot
be changed after the corpus is created.
The response message returns a unique id that you use to reference the
corpus. The name does not need to be unique within an account.
Filter Attribute
In order to reference metadata in filter expressions, you must define the filter attributes at the time of corpus creation. If you already created a corpus, use the Replace Filter Attributes API.
Filter attributes allow you to attach metadata to your data at the document (doc)
or part level, which you can use later in filter expressions to narrow the scope
of your queries.
A filter attribute must specify a unique name (up to 64 characters long), and
a level which indicates whether it exists in the doc or part level
metadata. At indexing time, metadata with this name is extracted and made
available for filter expressions to operate on.
Doc and Part Filter Levels
The doc attribute applies to the entire document. Use this for metadata that
is consistent across the whole document, such as author, publication date, and
document ID.
The part attribute applies to specific sections or chunks within a document.
Use for metadata that may vary within different parts of the document, such as
sections, page numbers, and sentiment scores.
If indexed is true, the system will build an index on the extracted values
to further improve the performance of filter expressions involving the
attribute.
Filter attributes must specify a type, which is validated when
documents are indexed. The four supported types are integer, which stores
signed whole-number values up to eight bytes in length; real, for storing
floating point values in IEEE 754 8-byte format; text for storing
textual strings in UTF-8 encoding, and boolean for storing true/false
values.
After you define filter attributes, you can use them within your queries. For example:
- Document-level attribute:
doc.publication_year > 2020 - Part-level attribute:
part.sentiment_score > 0.7
REST 2.0 URL
Create Corpus REST 2.0 Endpoint
Vectara exposes a REST endpoint at the following URL to create a corpus:https://api.vectara.io/v2/corporaThe API Reference shows the full Create Corpus REST definition.
gRPC Example
You can find the full Create Corpus gRPC definition at admin.proto.
The CreateCorpusRequest message contains a Corpus message with the name,
description, and other customization options. The CreateCorpusResponse
provides the response with the new Corpus ID and status.